# Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation

arXiv: https://arxiv.org/abs/2207.02201 · HTML: https://mlsanity.com/paper/2207.02201/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 27 of 27)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://mlsanity.com/paper/1911.11236/) (2019)
- [Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution](https://mlsanity.com/paper/2007.16100/) (2020)
- [Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation](https://mlsanity.com/paper/2011.10033/) (2020)
- [FlowNet3D: Learning Scene Flow in 3D Point Clouds](https://mlsanity.com/paper/1806.01411/) (2018)
- [SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving](https://mlsanity.com/paper/2003.03653/) (2020)
- [RangeDet:In Defense of Range View for LiDAR-based 3D Object Detection](https://mlsanity.com/paper/2103.10039/) (2021)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [Motion Guided Attention for Video Salient Object Detection](https://mlsanity.com/paper/1909.07061/) (2019)
- [Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data](https://mlsanity.com/paper/2105.08971/) (2021)
- [Towards Segmenting Anything That Moves](https://mlsanity.com/paper/1902.03715/) (2019)
- [Video Object Segmentation and Tracking: A Survey](https://mlsanity.com/paper/1904.09172/) (2019)
- [Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform](https://mlsanity.com/paper/2008.09162/) (2020)
- [BirdNet: a 3D Object Detection Framework from LiDAR information](https://mlsanity.com/paper/1805.01195/) (2018)
- [ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building](https://mlsanity.com/paper/2103.04316/) (2021)
- [Automatic Labeling to Generate Training Data for Online LiDAR-based Moving Object Segmentation](https://mlsanity.com/paper/2201.04501/) (2022)
- [Refining activation downsampling with SoftPool](https://mlsanity.com/paper/2101.00440/) (2021)
- [Range Image-based LiDAR Localization for Autonomous Vehicles](https://mlsanity.com/paper/2105.12121/) (2021)
- [LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation](https://mlsanity.com/paper/2111.04875/) (2021)
- [Inferring Objectives in Continuous Dynamic Games from Noise-Corrupted Partial State Observations](https://mlsanity.com/paper/2106.03611/) (2021)
- [Mapless Online Detection of Dynamic Objects in 3D Lidar](https://mlsanity.com/paper/1809.06972/) (2018)
- [MUNet: Motion Uncertainty-aware Semi-supervised Video Object Segmentation](https://mlsanity.com/paper/2111.14646/) (2021)

## Built on by (most cited first, 29 of 29)

- [OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds](https://mlsanity.com/paper/2210.04458/) (2022)
- [MF-MOS: A Motion-Focused Model for Moving Object Segmentation](https://mlsanity.com/paper/2401.17023/) (2024)
- [InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data](https://mlsanity.com/paper/2303.03909/) (2023)
- [TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation](https://mlsanity.com/paper/2309.07849/) (2023)
- [MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model](https://mlsanity.com/paper/2404.12794/) (2024)
- [HiMo: High-Speed Objects Motion Compensation in Point Clouds](https://mlsanity.com/paper/2503.00803/) (2025)
- [PCPNet: An Efficient and Semantic-Enhanced Transformer Network for Point Cloud Prediction](https://mlsanity.com/paper/2304.07773/) (2023)
- [DUFOMap: Efficient Dynamic Awareness Mapping](https://mlsanity.com/paper/2403.01449/) (2024)
- [MotionBEV: Attention-Aware Online LiDAR Moving Object Segmentation with Bird's Eye View based Appearance and Motion Features](https://mlsanity.com/paper/2305.07336/) (2023)
- [Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds](https://mlsanity.com/paper/2309.16435/) (2023)
- [A Dynamic Points Removal Benchmark in Point Cloud Maps](https://mlsanity.com/paper/2307.07260/) (2023)
- [BeautyMap: Binary-Encoded Adaptable Ground Matrix for Dynamic Points Removal in Global Maps](https://mlsanity.com/paper/2405.07283/) (2024)
- [SegNet4D: Efficient Instance-Aware 4D Semantic Segmentation for LiDAR Point Cloud](https://mlsanity.com/paper/2406.16279/) (2024)
- [HeLiMOS: A Dataset for Moving Object Segmentation in 3D Point Clouds From Heterogeneous LiDAR Sensors](https://mlsanity.com/paper/2408.06328/) (2024)
- [Instantaneous Perception of Moving Objects in 3D](https://mlsanity.com/paper/2405.02781/) (2024)
- [StreamMOS: Streaming Moving Object Segmentation with Multi-View Perception and Dual-Span Memory](https://mlsanity.com/paper/2407.17905/) (2024)
- [LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving](https://mlsanity.com/paper/2307.08850/) (2023)
- [Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds](https://mlsanity.com/paper/2507.03441/) (2025)
- [Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds](https://mlsanity.com/paper/2507.03463/) (2025)
- [SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds](https://mlsanity.com/paper/2507.06906/) (2025)
- [MV-MOS: Multi-View Feature Fusion for 3D Moving Object Segmentation](https://mlsanity.com/paper/2408.10602/) (2024)
- [CV-MOS: A Cross-View Model for Motion Segmentation](https://mlsanity.com/paper/2408.13790/) (2024)
- [Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation](https://mlsanity.com/paper/2410.06893/) (2024)
- [Moving Object Segmentation in Point Cloud Data using Hidden Markov Models](https://mlsanity.com/paper/2410.18638/) (2024)
- [Head and Neck Tumor Segmentation of MRI from Pre- and Mid-radiotherapy with Pre-training, Data Augmentation and Dual Flow UNet](https://mlsanity.com/paper/2412.14846/) (2024)
- [HIF: Height Interval Filtering for Efficient Dynamic Points Removal](https://mlsanity.com/paper/2503.06863/) (2025)
- [Temporal Overlapping Prediction: A Self-supervised Pre-training Method for LiDAR Moving Object Segmentation](https://mlsanity.com/paper/2503.07167/) (2025)
- [KDMOS:Knowledge Distillation for Motion Segmentation](https://mlsanity.com/paper/2506.14130/) (2025)
- [Self-Supervised Moving Object Segmentation of Sparse and Noisy Radar Point Clouds](https://mlsanity.com/paper/2511.02395/) (2025)

## Abstract (arXiv)

Accurate moving object segmentation is an essential task for autonomous driving. It can provide effective information for many downstream tasks, such as collision avoidance, path planning, and static map construction. How to effectively exploit the spatial-temporal information is a critical question for 3D LiDAR moving object segmentation (LiDAR-MOS). In this work, we propose a novel deep neural network exploiting both spatial-temporal information and different representation modalities of LiDAR scans to improve LiDAR-MOS performance. Specifically, we first use a range image-based dual-branch structure to separately deal with spatial and temporal information that can be obtained from sequential LiDAR scans, and later combine them using motion-guided attention modules. We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects. We verify the effectiveness of our proposed approach on the LiDAR-MOS benchmark of SemanticKITTI. Our method outperforms the state-of-the-art methods significantly in terms of LiDAR-MOS IoU. Benefiting from the devised coarse-to-fine architecture, our method operates online at sensor frame rate. The implementation of our method is available as open source at: https://github.com/haomo-ai/MotionSeg3D.